#ifndef CAFFE_SIGMOID_LAYER_HPP_
#define CAFFE_SIGMOID_LAYER_HPP_

#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"


namespace caffe {

/* @brief Sigmoid function non-linearity @f$ y = (1 + \exp(-x))^{-1} @f$, a classic choice in neural networks.
 * Note that the gradient vanishes as the values move away from 0.
 * The ReLULayer is often a better choice for this reason. */
template <typename Dtype>
class SigmoidLayer : public NeuronLayer<Dtype> {
 public:
  explicit SigmoidLayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {}
  virtual inline const char* type() const { return "Sigmoid"; }

 protected:
  /* @param bottom input Blob vector (length 1) -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the computed outputs @f$ y = (1 + \exp(-x))^{-1} @f$ */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);

  /* @brief Computes the error gradient w.r.t. the sigmoid inputs.
   * @param top output Blob vector (length 1), providing the error gradient with respect to the outputs
   *   -# @f$ (N \times C \times H \times W) @f$ containing error gradients @f$ \frac{\partial E}{\partial y} @f$
   *      with respect to computed outputs @f$ y @f$
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$; Backward fills their diff with
   *      gradients @f$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y} y (1 - y)
   *      @f$ if propagate_down[0] */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
};

}  // namespace caffe
#endif  // CAFFE_SIGMOID_LAYER_HPP_
